# The BP Dependency Function: a Generic Measure of Dependence between Random Variables

arXiv: https://arxiv.org/abs/2203.12329 · HTML: https://mlsanity.com/paper/2203.12329/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 19 references that are in the map.
- First posted on arXiv 23 Mar 2022.

## Builds on (most cited first, 1 of 1)

- [Mutual Dependence: A Novel Method for Computing Dependencies Between Random Variables](https://mlsanity.com/paper/1506.00673/) (2015)

## Built on by (most cited first, 1 of 1)

- [The Berkelmans-Pries Feature Importance Method: A Generic Measure of Informativeness of Features](https://mlsanity.com/paper/2301.04740/) (2023)

## Abstract (arXiv)

Measuring and quantifying dependencies between random variables (RV's) can give critical insights into a data-set. Typical questions are: `Do underlying relationships exist?', `Are some variables redundant?', and `Is some target variable Y highly or weakly dependent on variable X?' Interestingly, despite the evident need for a general-purpose measure of dependency between RV's, common practice of data analysis is that most data analysts use the Pearson correlation coefficient (PCC) to quantify dependence between RV's, while it is well-recognized that the PCC is essentially a measure for linear dependency only. Although many attempts have been made to define more generic dependency measures, there is yet no consensus on a standard, general-purpose dependency function. In fact, several ideal properties of a dependency function have been proposed, but without much argumentation. Motivated by this, in this paper we will discuss and revise the list of desired properties and propose a new dependency function that meets all these requirements. This general-purpose dependency function provides data analysts a powerful means to quantify the level of dependence between variables. To this end, we also provide Python code to determine the dependency function for use in practice.